# Development of deep learning methods to optimize patient personalized treatment for craniosynostosis

> **NIH NIH F31** · UNIVERSITY OF COLORADO DENVER · 2024 · $37,721

## Abstract

Project Summary/Abstract
 Craniosynostosis is the premature fusion of one or more cranial sutures. The growth at the cranial plates
normally separated by the fused suture is arrested, resulting in compensatory overgrowth parallel to the fused
suture. This abnormal development causes head malformations and can lead to increased intracranial pressure
and developmental complications. Patients with this condition normally undergo surgical treatment to remove
the growth constraints and create more aesthetically normative head shapes. However, the traditional evaluation
of these patients after treatment has been based on subjective clinical expertise. During the last decade, three-
dimensional (3D) photogrammetry has gained popularity to evaluate craniofacial anomalies, but existing
methods to analyze this data have failed in their clinical translation due to the use of inefficient and inaccurate
processing techniques. Hence, there is a lack of quantitative evidence to evaluate and compare the continuous
process of head shape normalization between different surgical treatments. This proposal aims to develop new
geometric deep learning methods to enable the fully automated, real-time evaluation of head shapes using 3D
photogrammetry at the clinic and will address the lack of quantitative evidence in the objective assessment of
surgical outcomes. The first aim of this proposal is to characterize the normalization of head shape after
corrective surgery for different treatment approaches. A statistical model of head shape normalization for each
surgical approach will be created to quantify novel metrics of head shape anomaly and probabilistic risk of
craniosynostosis. This model will be the first to incorporate the effects of age at surgery, sex, and the pre-surgical
severity of head shape anomalies on the normalization of head shape. The second aim of this proposal is to
develop a personalized geometric deep learning framework to determine the optimal surgical approach for each
patient with craniosynostosis. This aim will incorporate a novel context-encoding geometric deep learning
method to estimate the expected post-surgical head shape normalization for each potential surgical treatment
and identify the optimal surgical treatment for every patient based on objective retrospective data. The results
from these aims will enable the data-driven, personalized, and objective assessment of surgical treatment for
craniosynostosis that can be used to optimize patient management. This proposal includes a comprehensive
training plan consisting of mentored computational training in the development of deep learning methods,
mentored clinical and translational research training at Children’s Hospital Colorado, and didactic coursework in
statistical and machine learning methods. This proposal is uniquely positioned to be successful given the
multidisciplinary environment at the University of Colorado Anschutz Medical Campus with resources in
translational research at ...

## Key facts

- **NIH application ID:** 10994923
- **Project number:** 1F31DE033614-01A1
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Connor Elkhill
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $37,721
- **Award type:** 1
- **Project period:** 2024-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10994923

## Citation

> US National Institutes of Health, RePORTER application 10994923, Development of deep learning methods to optimize patient personalized treatment for craniosynostosis (1F31DE033614-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10994923. Licensed CC0.

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